Construction Of Knowledge Base Research Articles

Exploring the Role of Self-Adaptive Feature Words in Relation Quintuple Extraction for Scientific Literature

Extracting relation quintuple and feature words from unstructured text is a prelude to the construction of the scientific knowledge base. At present, the prior works use explicit clues between entities to study this task but ignore the use and the association of the feature words. In this work, we propose a new method to generate self-adaptive feature words from the original text for every single sample. These words can add additional correlation information to the knowledge graph. We allow the model to generate a new word representation and apply it to the original sentence to judge the relation type and locate the head and tail of the relation quintuple. Compared with the previous works, the feature words increase the flexibility of relying on information and improve the explanatory ability. Extensive experiments on scientific field datasets illustrate that the self-adaptive feature words method (SAFW) is good at ferreting out the unique feature words and obtaining the core part for the quintuple. It achieves good performance on four public datasets and obtains a markable performance improvement compared with other baselines.

Joint extraction of wheat germplasm information entity relationship based on deep character and word fusion

The verified text data of wheat varieties is an important component of wheat germplasm information. To automatically obtain a structured description of the phenotypic and genetic characteristics of wheat varieties, the aim at solve the issues of fuzzy entity boundaries and overlapping relationships in unstructured wheat variety approval data, WGIE-DCWF (joint extraction model of wheat germplasm information entity relationship based on deep character and word fusion) was proposed. The encoding layer of the model deeply fused word semantic information and character information using the Transformer encoder of BERT. This allowed for the cascading fusion of contextual semantic feature information to achieve rich character vector representation and improve the recognition ability of entity features. The triple extraction layer of the model established a cascading pointer network, extracted the head entity, extracted the tail entity according to the relationship category, and decoded the output triplet. This approach improved the model’s capability to extract overlapping relationships. The experimental results demonstrated that the WGIE-DCWF model performed exceptionally well on both the WGD (wheat germplasm dataset) and the public dataset DuIE. The WGIE-DCWF model not only achieved high performance on the evaluation datasets but also demonstrated good generalization. This provided valuable technical support for the construction of a wheat germplasm information knowledge base and is of great significance for wheat breeding, genetic research, cultivation management, and agricultural production.

Product Improvement Using Knowledge Mining and Effect Analogy

Different from new product development, design improvement aims to solve the problems of existing products. Although design knowledge and effect tools have been applied in product improvement, the existing methods for design improvement are limited in their specific application areas. A general method of product improvement is proposed in this paper using the knowledge mining and effect analogy. The length–time dimension is introduced to link the problem analysis and problem-solving for the first time. This method includes the effect knowledge base construction, length–time dimension extraction, effect retrieval, effect ranking, analogy object selection, and effect structure mapping. This method integrates a variety of algorithms and software tools in design knowledge mining to improve the efficiency of the effect analogy for product improvement. Through the comparative analysis of three effect retrieval methods and design improvement of a button battery ring device, the superiority and feasibility of the proposed method are verified.

Quality Evaluation of Triples in Knowledge Graph by Incorporating Internal With External Consistency.

The evaluation of knowledge quality (KQ) in multisource knowledge graphs (KGs) is an essential step for many applications, such as fragmented knowledge fusion and knowledge base construction. Many existing quality evaluation methods for multisource knowledge are based on validation from high-quality knowledge bases or statistical analysis of knowledge related to a specific fact from multiple sources, named external consistency (EC)-based methods. However, high-quality KGs are difficult to obtain, and there might exist incorrect knowledge in multisource KGs interfering with KQ evaluation. To address the issue, this article refers to the internal structure of a KG to evaluate the degree to which the contained triples conform to the overall semantic pattern of the KG, such as KG embedding and logic inference-based approaches, defined as internal consistency (IC) evaluation. The IC is integrated with the EC to identify possible incorrect triples and reduce their influences on the KQ evaluation, thus alleviating the interference of incorrect knowledge. The proposed method is verified with multiple datasets, and the results demonstrate that the proposed method could significantly reduce wrong evaluations caused by incorrect knowledge and effectively improve the quality evaluation of triples.

A novel fracture-cavity reservoir outcrop geological knowledge base construction method considering parameter collection and processing, mutual transformation of data-knowledge, application and update

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base (GKB) tailored to fracture-cavity reservoir outcrops within the North Tarim Basin. The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations, thin section studies, unmanned aerial vehicle scanning, and high-resolution cameras. Subsequently, a three-dimensional digital outcrop model was generated, and the parameters were standardized. An assessment of traditional geological knowledge was conducted to delineate the knowledge framework, content, and system of the GKB. The basic parameter knowledge was extracted using multiscale fine characterization techniques, including core statistics, field observations, and microscopic thin section analysis. Key mechanism knowledge was identified by integrating trace elements from filling, isotope geochemical tests, and water-rock simulation experiments. Significant representational knowledge was then extracted by employing various methods such as multiple linear regression, neural network technology, and discriminant classification. Subsequently, an analogy study was performed on the karst fracture-cavity system (KFCS) in both outcrop and underground reservoir settings. The results underscored several key findings: (1) Utilization of a diverse range of techniques, including outcrop observations, core statistics, unmanned aerial vehicle scanning, high-resolution cameras, thin section analysis, and electron scanning imaging, enabled the acquisition and standardization of data. This facilitated effective management and integration of geological parameter data from multiple sources and scales. (2) The GKB for fracture-cavity reservoir outcrops, encompassing basic parameter knowledge, key mechanism knowledge, and significant representational knowledge, provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs. (3) The developmental characteristics of fracture-cavities in karst outcrops offer effective, efficient, and accurate guidance for fracture-cavity research in underground karst reservoirs. The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide.

Promoting high-quality development of acupuncture and moxibustion.

In the past 20 years, the acupuncture-moxibustion discipline has made a great progress in clinical research, method construction, standard formulation, guideline promotion, basic theory and key scientific issue research. Internationally, the development of acupuncture and moxibustion has gradually begun to pay more attention to the basic issues of the discipline itself from focusing on clinical evidence. The National Institute of Health of USA pays close attention to the construction of acupoint knowledge base and database and to the transformation of peripheral nerve stimulation techniques, which brings forth opportunities and challenges for the development of acupuncture-moxibustion discipline. In the present paper, we analyze the shortcomings of the current development of acupuncture and moxibustion, put forward some strategies for high-quality development in the future, and sort out the basic scientific issues to form an academic consensus. We should employ modern scientific language to express the scientific connotations of the basic theory of acupuncture and moxibustion, and build an open and self-consistent modern theoretical system. In addition, we also should attract more multidisciplinary talents to harmoniously and assiduously work together, insist on continuous innovation to open up a new situation in the transformation of basic scientific research achievements, and establish a new theoretical system of "somato-medicine" represented by acupuncture and moxibustion. In this way, we will guide the acupuncture-moxibustion discipline to make an original contribution to the modern life science and future medicine.

Knowledge reasoning in power grid infrastructure projects based on deep multi-view graph convolutional network

With the rapid development of power grid infrastructure, especially the increasing number of ultra-high voltage (UHV) projects, knowledge extracted from historical engineering data is collected and can be potentially used to assist in the review of power transmission and transformation projects. However, conventional knowledge modeling and knowledge reasoning methods cannot meet the current needs of power grid construction. In this paper, considering the more supernumerary and distinctive information brought by multi-view data which could be beneficial for feature representation and knowledge reasoning from the constructed knowledge base, a multi-view graph convolutional network (GCN) based on knowledge graph is proposed to make classification for power grid infrastructure projects. Specifically, several views are constructed based on attribute information of a knowledge graph. In addition, a Haar convolution-based pooling mechanism is employed to capture the structural features represented by a chain of subgraphs. And then an aggregator that combines both attribute and structural information is used to classify UHV projects. Results from both UHV and NCI-1 datasets indicate that our proposed method is more has higher accuracy and generalization ability.

MatSciRE: Leveraging pointer networks to automate entity and relation extraction for material science knowledge-base construction

Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc. Automatically extracting this information to generate a material science knowledge base is a challenging task. In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder–decoder framework, to jointly extract entities and relations from material science articles as a triplet (entity1,relation,entity2). Specifically, we target the battery materials and identify five relations to work on — conductivity, coulombic efficiency, capacity, voltage, and energy. Our proposed approach achieved a much better F1-score (0.771) than a previous attempt using ChemDataExtractor (0.716). The overall graphical framework of MatSciRE is shown in Fig. 1. The material information is extracted from material science literature in the form of entity–relation triplets using MatSciRE.

Information Resilience in Networks: An Analysis of a National Security Legislation Evidence Base

Governance theory emphasises that networks are pivotal to societal governance and related steering mechanisms. One important means of steering is legislation, the evidence base of which is built on the interaction between social actors and the expert hearings related to the legislative process. This research uses network analysis to examine the construction of a legislative knowledge base and the information resilience displayed in the related networks. The data are derived from experts (n=440) appearing in committee hearings on five proposed acts of parliament. The results show that the expert consultations behind the legislation are official-oriented and illuminate the limited information produced by the private sector and a narrow view of scientific expertise. The network reveals epistemic authorities – mainly representing the security cluster – acting as gatekeepers. A more systemic approach is needed to build an evidence base encompassing the views on societal phenomena derived from different disciplines.

An Artificial Intelligence-Based Model for Knowledge Evaluation and Integration in Public Organizations

In the construction of knowledge bases, it is very important to evaluate the quality of the knowledge entered into them. This is exacerbated in public administrations, where knowledge should be oriented towards public services. In this study, an artificial intelligence-based method for the evaluation of knowledge is described. This method takes advantage of the structure and contents of the knowledge representation schemas (representing the knowledge of the corresponding experts) to carry out knowledge evaluation. More precisely, the method allows the various comparisons between the schemas to be integrated and the overall schema to evaluate the contribution of each schema.

Keyword Extraction-based Library Intelligence Services: Challenges, Adaptations and Reinvention

Abstract This paper proposes a domain knowledge base construction platform for thematic intelligence services of library intelligence institutions. The platform proposes a keyword extraction algorithm applicable to the literature resources of library intelligence services based on a statistical model and N-Gram model combined with feature word information. The automatic summary algorithm based on statistics combines the information of sentence content and location to comprehensively evaluate the weights of candidate summary sentences and achieve automatic summary extraction. Based on the HDP topic model, a literature recommendation model oriented to the resource retrieval context is proposed, which can convert the literature feature similarity calculation to the topic similarity calculation with implied literature feature semantics from the perspective of semantic analysis and improve the accuracy of recommended literature. The technical feasibility of this system platform is verified through relevant data experiments. Regarding the accuracy of the literature recommendation, the platform of this paper and the traditional method are 86.36% and 75.16%, respectively, and the recall rate is 67.29% and 57.25%, respectively. The platform scored 3.85 on the comprehensive evaluation of library intelligence service, close to the excellent level. Therefore, the platform can realize various functions, from resource collection and organization to resource processing and utilization, with certain practical significance.

Construction and Application of a Complex Network-Based Case Knowledge Base in an Assisted Instruction System

With the development of the business administration major, case teaching has become an effective teaching method. However, a lot of case resources exist in the business administration field, and the correlation between these cases is complex, which poses challenges to teachers and students in terms of information overload and knowledge organization. Existing case knowledge bases in assisted instruction systems often have shortcomings in terms of knowledge relationship strength calculation, text semantic similarity calculation, hierarchical knowledge clustering, and propagation evolution. To solve these problems, this study proposed a method of constructing and applying a case knowledge base in an assisted instruction system based on a complex network. This method mainly includes three aspects: calculation and reasoning of ontology relationship strength, calculation of ontology text semantic similarity, and hierarchical knowledge clustering and propagation evolution in complex networks. Through a comprehensive study of these three aspects, a more efficient and intelligent case knowledge base in an assisted instruction system was constructed, which not only improved the teaching efficiency and quality of the business administration major but also had the potential to promote teaching in other disciplines and fields. In addition, this study also provided new perspectives and methods in related fields, which is of great significance for efficiently organizing and utilizing knowledge.

A method for constructing a machining knowledge graph using an improved transformer

Process knowledge base is a core component in the intelligent process, which determines the intelligent degree of product manufacturing and directly affects the production efficiency of products. However, traditional process knowledge base is often constructed manually, which is difficult and time-consuming. In addition, in the field of machining, there is a large amount of unstructured invisible process knowledge, which is not effectively organized and managed. To make use of this knowledge and provide knowledge support for downstream production and maintenance, a process knowledge base construction framework is proposed by using Knowledge Graph (KG) technology. Firstly, the ontology rules of process knowledge are designed from the perspective of the processing method of process characteristics according to the particularity of knowledge in the machining field. The process KG schema layer is then constructed. Secondly, a neural network BERT–Improved TRANSFORMER–CRF (BITC) model is proposed for the machining knowledge extraction task, and the data layer is constructed. Then, entity linking and knowledge fusion are performed by using the word vector cosine similarity algorithm and stored in Neo4j. The process KG is then constructed. Finally, the effectiveness of the proposed method is verified by using an aero-engine casing of an enterprise as an example. Under the same dataset, the BITC model scored higher than several other classical models. The Precision, Recall, and F1-score were 85.27%, 86.40%, and 85.83 %, respectively.

Knowledge base construction for the semantic management of environment-enriched built heritage: The case of Algerian traditional houses architecture

Traditional domestic architecture in Algeria reflects ancestral know-how which could serve as a model for new architectural achievements concerned with respecting the environment. This multidisciplinary knowledge covering different contexts (architectural, physical and environmental), is available but scattered through various sources of different formats (texts, tables, illustrations, etc.). Capturing such knowledge through an efficient knowledge repository is required. Knowledge base (KB) repositories have shown many significant contributions for managing the complexity, the variety of contexts and the querying of Built Heritage (BH) data. Creating a KB is a complex process that requires identifying and extracting the relevant knowledge from raw sources of information, then structuring, cleaning and integrating the extracted information into the KB repository. The management of these issues and their inter-dependencies are not always detailed in related BH literature. We propose in this paper a complete process for creating a KB dedicated to the built heritage consisting of Algerian traditional houses. Two main contributions are emphasized in this study: (i) the construction process of the KB is detailed at each step using two main suites: the ontology suite for identifying and structuring the relevant concepts, and the data suite that allows the flow of knowledge from the sources to the KB. (ii) the semantic variety of contexts of the input sources, their linguistic complexity and their format heterogeneity is handled all along the process, and is reflected at two levels: at the schema level (ontology model), and at the data level using automatic information extraction techniques. The definition of the KB includes fine-grained provenance metadata. Different results are proposed to evaluate the content and the quality of the KB.

MAPS-KB: A Million-Scale Probabilistic Simile Knowledge Base

The ability to understand and generate similes is an imperative step to realize human-level AI. However, there is still a considerable gap between machine intelligence and human cognition in similes, since deep models based on statistical distribution tend to favour high-frequency similes. Hence, a large-scale symbolic knowledge base of similes is required, as it contributes to the modeling of diverse yet unpopular similes while facilitating additional evaluation and reasoning. To bridge the gap, we propose a novel framework for large-scale simile knowledge base construction, as well as two probabilistic metrics which enable an improved understanding of simile phenomena in natural language. Overall, we construct MAPS-KB, a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. We conduct sufficient experiments to justify the effectiveness and necessity of the methods of our framework. We also apply MAPS-KB on three downstream tasks to achieve state-of-the-art performance, further demonstrating the value of MAPS-KB. Resources of MAPS-KB are publicly available at https://github.com/Abbey4799/MAPS-KB.

Construction of a 3D Model Knowledge Base Based on Feature Description and Common Sense Fusion

Three-dimensional models represent the shape and appearance of real-world objects in a virtual manner, enabling users to obtain a comprehensive and accurate understanding by observing their appearance from multiple perspectives. The semantic retrieval of 3D models is closer to human understanding, but semantic annotation for describing 3D models is difficult to automate, and it is still difficult to construct an easy-to-use 3D model knowledge base. This paper proposes a method for building a 3D model knowledge base to enhance the ability to intelligently manage and reuse 3D models. The sources of 3D model knowledge are obtained from two aspects: on the one hand, constructing mapping rules between the 3D model features and semantics, and on the other hand, extraction from a common sense database. Firstly, the viewpoint orientation is established, the semantic transformation rules of different feature values are established, and the representation degree of different features is divided to describe the degree of the contour approximating the regular shape under different perspectives through classification. An automatic output model semantic description of the contour is combined with spatial orientation. Then, a 3D model visual knowledge ontology is designed from top to bottom based on the upper ontology of the machine-readable comprehensive knowledge base and the relational structure of the ConceptNet ontology. Finally, using a weighted directed graph representation method with a sparse-matrix-integrated semantic dictionary as a carrier, an entity dictionary and a relational dictionary are established, covering attribute names and attribute value data. The sparse matrix is used to record the index information of knowledge triplets to form a three-dimensional model knowledge base. The feasibility of this method is demonstrated by semantic retrieval and reasoning on the label meshes dataset and the cultural relics dataset.

From centralized to ad-hoc knowledge base construction for hypotheses generation

ObjectiveTo demonstrate and develop an approach enabling individual researchers or small teams to create their own ad-hoc, lightweight knowledge bases tailored for specialized scientific interests, using text-mining over scientific literature, and demonstrate the effectiveness of these knowledge bases in hypothesis generation and literature-based discovery (LBD). MethodsWe propose a lightweight process using an extractive search framework to create ad-hoc knowledge bases, which require minimal training and no background in bio-curation or computer science. These knowledge bases are particularly effective for LBD and hypothesis generation using Swanson's ABC method. The personalized nature of the knowledge bases allows for a somewhat higher level of noise than “public facing” ones, as researchers are expected to have prior domain experience to separate signal from noise. Fact verification is shifted from exhaustive verification of the knowledge base to post-hoc verification of specific entries of interest, allowing researchers to assess the correctness of relevant knowledge base entries by considering the paragraphs in which the facts were introduced. ResultsWe demonstrate the methodology by constructing several knowledge bases of different kinds: three knowledge bases that support lab-internal hypothesis generation: Drug Delivery to Ovarian Tumors (DDOT); Tissue Engineering and Regeneration; Challenges in Cancer Research; and an additional comprehensive, accurate knowledge base designated as a public resource for the wider community on the topic of Cell Specific Drug Delivery (CSDD). In each case, we show the design and construction process, along with relevant visualizations for data exploration, and hypothesis generation. For CSDD and DDOT we also show meta-analysis, human evaluation, and in vitro experimental evaluation. ConclusionOur approach enables researchers to create personalized, lightweight knowledge bases for specialized scientific interests, effectively facilitating hypothesis generation and literature-based discovery (LBD). By shifting fact verification efforts to post-hoc verification of specific entries, researchers can focus on exploring and generating hypotheses based on their expertise. The constructed knowledge bases demonstrate the versatility and adaptability of our approach to versatile research interests. The web-based platform, available at https://spike-kbc.apps.allenai.org, provides researchers with a valuable tool for rapid construction of knowledge bases tailored to their needs.

A Co-adaptive Duality-aware Framework for Biomedical Relation Extraction.

Biomedical relation extraction is a vital task for electronic health record mining and biomedical knowledge base construction. Previous work often adopts pipeline methods or joint methods to extract subject, relation, and object while ignoring the interaction of subject-object entity pair and relation within the triplet structure. However, we observe that entity pair and relation within a triplet are highly related, which motivates us to build a framework to extract triplets that can capture the rich interactions among the elements in a triplet. We propose a novel co-adaptive biomedical relation extraction framework based on a duality-aware mechanism. This framework is designed as a bidirectional extraction structure that fully takes interdependence into account in the duality-aware extraction process of subject-object entity pair and relation. Based on the framework, we design a co-adaptive training strategy and a co-adaptive tuning algorithm as collaborative optimization methods between modules to promote better mining framework performance gain. The experiments on two public datasets show that our method achieves the best F1 among all state-of-the-art baselines and provides strong performance gain on complex scenarios of various overlapping patterns, multiple triplets, and cross-sentence triplets. Code is available at https://github.com/11101028/CADA-BioRE. Supplementary data are available at Bioinformatics online.

AJE: Attention Mechanism for Entity-relation Joint Extraction

Joint extraction of entities and relations is an significant issue of information extraction, which is very helpful for many downstream tasks, including knowledge base construction, question answering, and biomedical text diagnosis[1], etc. The common approach of existing models is to extract the subject and the relation first, then compute the subject and the relation to obtain the object, and finally, the triplet is judged. However, such an approach cannot efficiently handle of information extraction, and the results are not very good for Subject-Object Overlap (SOO) case. In this paper, a joint entity-relation extraction method AJE is proposed based on dot-product attention mechanism. The method first maps subject, object and relationship into three matrices of Q, K and V. After that, the attention weighting is achieved on these three matrices and the output vector is used to determine whether the triple is correct or not. The F1-score is used in experiments to show that the proposed model is more efficient than the current existing ones. It also has better results in handling other cases such as SOO, multi-triple problem, etc.

A semantic data-driven knowledge base construction method to assist designers in design inspiration based on traditional motifs

Directing at the problem of inaccurate expression of tacit knowledge and low heuristic of retrieval results in the design inspiration drawing process of designers based on traditional motifs, a semantic data representation method of traditional motifs knowledge is put forward, and an effective knowledge base construction and knowledge retrieval method for traditional motifs are described. First, the connotation of traditional motifs is deeply explored based on the theory of symbol semantics, realizing the interpretation and transfer of the semantic features in traditional motifs. Second, shape grammar is adopted for fractal traditional motifs, the hierarchical structure of traditional motifs is built, and the parametric coding of semantic features of traditional motifs is completed, providing the semantic data representation method of traditional motifs knowledge. Third, based on the clustering algorithm, the construction method of the traditional motifs knowledge base is explained, and a traditional motifs knowledge retrieval method taking into account the user's perceptual preference is proposed. Finally, setting the traditional Chinese motifs as an example, with a tea set as the designed product, the construction process of a small traditional motif knowledge base is shown in detail, proving the feasibility of this paper’s theory. Compared with the traditional theory, the semantic data representation method of traditional pattern knowledge is greatly advantageous in semantic differences elimination and computational efficiency.